Creates an interaction measurement item using a two-stage approach. The two-stage procedure for both PLS and CBSEM models estimates construct scores in the first stage, and uses them to produce a single-item product item for the interaction term in the second stage. For a PLS model, the first stage uses PLS to compute construct scores. For a CBSEM model, the first stage uses a CFA to produce ten Berge construct scores.
Creates an interaction measurement item using a two-stage approach. The two-stage procedure for both PLS and CBSEM models estimates construct scores in the first stage, and uses them to produce a single-item product item for the interaction term in the second stage. For a PLS model, the first stage uses PLS to compute construct scores. For a CBSEM model, the first stage uses a CFA to produce ten Berge construct scores.
# two stage approach as per Henseler & Chin (2010): two_stage(iv, moderator, weights)
iv |
The independent variable that is subject to moderation. |
moderator |
The moderator variable. |
weights |
is the relationship between the items and the interaction terms. This can be
specified as |
An un-evaluated function (promise) for estimating a two-stage interaction effect.
Henseler & Chin (2010), A comparison of approaches for the analysis of interaction effects between latent variables using partial least squares path modeling. Structural Equation Modeling, 17(1),82-109.
data(mobi)
# seminr syntax for creating measurement model
mobi_mm <- constructs(
composite("Image", multi_items("IMAG", 1:5)),
composite("Expectation", multi_items("CUEX", 1:3)),
composite("Value", multi_items("PERV", 1:2)),
composite("Satisfaction", multi_items("CUSA", 1:3)),
interaction_term(iv = "Image", moderator = "Expectation", method = two_stage)
)
# structural model: note that name of the interactions construct should be
# the names of its two main constructs joined by a '*' in between.
mobi_sm <- relationships(
paths(to = "Satisfaction",
from = c("Image", "Expectation", "Value",
"Image*Expectation"))
)
# PLS example:
mobi_pls <- estimate_pls(mobi, mobi_mm, mobi_sm)
summary(mobi_pls)
# CBSEM example:
mobi_cbsem <- estimate_cbsem(mobi, as.reflective(mobi_mm), mobi_sm)
summary(mobi_cbsem)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.